Time compression in the supply chain: information management is
the vital ingredient
Rachel Mason-Jones, Denis R. Towill
The Authors
Rachel Mason-Jones, Researcher and Denis R. Towill is Professor, both at the Logistics Systems
Dynamics Group, University of Wales, Cardiff, UK
Denis R. Towill, Professor, both at the Logistics Systems Dynamics Group, University of Wales, Cardiff,
UK
Abstract
Our total cycle time (TCT) compression strategy encompasses the whole system in the supply chain from
consumer demand to customer satisfaction. TCT has two major components that are essential to meeting
customer demand: information flow and material flow. Both are necessities and together make up the
total supply chain lead-time; the information activates the material pipeline. Therefore to optimise a time
compression strategy TCT must include both the information and material flows. We show in the paper
that a very effective way of achieving TCT is via access to EPoS data by all "players" in the supply chain.
The tremendous benefits exhibited by TCT compression within the supply chain can be described as
"squaring the dynamic response circle". Not only are the stock dynamic responses improved via time
compression, but the capacity dynamics are also radically improved. Therefore TCT compression avoids
the dilemma frequently faced by companies when implementing change of having to trade off customer
service level against capacity utilisation. Our results are verified using a simulation model of a common
real-world supply chain.
Article type: Theoretical with Application in Practice.
Keywords: Bpr, Cycle Time, Decision-Support Systems, Logistics, Supply Chain Management.
Content Indicators: Research Implications** Practice Implications*** Originality** Readability**
Logistics Information Management
Volume 11 Number 2 1998 pp. 93-104
Copyright © MCB University Press ISSN 0957-6053
Introduction
It has been consistently argued that time is the competitive weapon of the 1990s (Stalk and Hout, 1990).
Unfortunately many companies take a restrictive view of time compression which they link purely with
production cycle time reduction. However, Thomas (1990) utilises the phrase total cycle time (TCT)
stating that "... the first word of Total Cycle Time was intended to express how short cycle times can be
applied productively to all segments of a business, not just manufacturing efforts." This distinction is
important, hence TCT compression programmes should be directed at all the work processes linking
customer need to that demand being satisfied (Towill, 1996).
The consequential effect of reducing TCT is direct leverage on the bottom line. Lead-time consists of two
consecutive components through a supply chain, the order information pipeline and the material flow
pipeline. We confirm that material flow cycle time compression coupled with open information channels
will potentially have a much greater effect on supply chain competitiveness. Unlike production delays,
which are reliant on technological processes, the order information pipeline can in theory be
instantaneous from the marketplace to the upstream players (Mason-Jones and Towill, 1997). The
essential competitive benefit of marketplace data usage by each player in the supply chain is summarised
by Sabath (1995) as "When everyone plays from the same sheet music, delays are minimised". However,
sharing information is only simple in theory: in practice attitudes must also be changed. It does not help if
the other players in "our" supply chain are frequently regarded as "the enemy" (Macbeth and Ferguson,
1994).
In an increasingly global marketplace most companies are competing with relatively similar machines,
technology, and expertise. Consequently business strategy is becoming the cornerstone to establishing
the world class enterprise. In this article we show via the simulation of a real world supply chain that
implementing a holistic time compression strategy must include both production and order information
lead-time reduction to maximise competitive advantage. The simulation output is benchmarked for easy
visual reference when ranking alternative routes for business process re-engineering (BPR) the supply
chain. The provision of such benchmarks is important if future progress is to be made in improving the
many supply chains world-wide which are operating below par (Towill, 1996).
How powerful is time as a competitive strategy?
Ever since Henry Ford implemented the moving assembly line in his Detroit plant in 1913 time has
become a competitive issue. The moving assembly line in the Highland Park site improved cycle time by
50 per cent, reducing the initial 2.3 minutes to 1.19 minutes (Womack et al., 1990)). Ford had established
the framework for mass production and quickly realised two main advantages - the amount of human
effort per vehicle was reduced and the more the plant produced the higher the cost reduction on each car.
By the 1920s the innovations to the manufacturing process had reduced the cost to the consumer by twothirds. The precedent was thus set that time was an asset to be utilised to compete effectively for market
share. However, the Ford mass production paradigm for the exploitation of time led to "economies of
scale". The present exploitation of time is fundamentally different: TCT compression generates
"economies of scope" (Towill, 1997a).
With consumer choice being one of the major drivers of the competitive marketplace even the most loyal
customer may turn to a competitor if the preferred company cannot supply on demand. This facet of
consumer behaviour is summed up by Tunc and Gupta (1993), "... customers' demand for quality and
uniqueness is increasing. In addition, customers want their demands satisfied almost instantaneously,
making time an important competitive strategy for the 1990's". It was shown by Stalk and Hout (1990) that
under specific circumstances customers are even prepared to pay a price premium, as in the quoted case
of Atlas Doors, for fast response and ready availability of the right product. Atlas Doors offsets its higher
prices with a response time on average 66 per cent faster than their competitors.
Many companies have already achieved startling results from concentrating their efforts on improving
their response time to customer demand. Table I is an early Stalk and Hout (1990) example which clearly
highlights the tremendous competitive achievements possible from adopting a time-based strategy. It is
clearly shown that the consumer responds favourably to the company which supplies what he/she wants
when he/she wants it, leading to an improvement in market share (resulting in the amazing 33× growth
advantage cited by Citicorp) and increased profit margin.
One of the most discussed and indeed successful time-based competitors is Wal-Mart. By 1992 it was the
largest and highest profit retailer in the world despite the fact that it had a presence only in half of the
USA (Stalk et al., 1992). Wal-Mart's ability to compete so effectively is due to the fact that concentrating
on TCT has allowed the company to improve its inventory turnover and variety thereby maintaining
service levels while reducing stock investment. This has meant Wal-Mart could increase product choice to
the customer without investing in extra inventory (Stalk and Hout, 1990). The store can therefore offer
greater choice and cost savings to the customer, thus encompassing two key ingredients to a successful
supermarket.
The simulation model in supply chain design
The use of simulation as a systems engineering analysis tool to research and understand the impact of
supply chain dynamics on business performance was established well over 30 years ago by Forrester
(1960). It is widely regarded as a demonstration vehicle by management consultants (Stalk and Hout,
1990).
The simulation model used as a test vehicle in this paper is the well-documented automatic pipeline
inventory order-based production control system (APIOBPCS). Supply chain dynamics can be adequately
mimicked by using the APIOBPCS format to represent the activities at each echelon within the chain
(Mason-Jones et al., 1998). Such a simulation model has been verified on real world supply chains
encountered in the electronics products industry by Berry et al. (1995). Hence the APIOBPCS based
simulation model may be used confidently as a benchmark to demonstrate performance enhancement for
a wide range of practical supply chains. This can readily include all echelons up to any push-pull decoupling point.
The parameters within the simulation model were set preserving key relationships recognised from
previous research as good design values (John et al., 1995). In order to simulate a time compression
activity the base line model had arbitrarily increasingly longer lead-times progressing upstream in the
supply chain, as follows:
 Retailer lead-time = four weeks.
 Distributor lead-time = six weeks.
 Warehouse lead-time = eight weeks.
 Factory lead-time = ten weeks.
It should be noted that the replenishment order cycle time for the simulation has been arbitrarily set at
one week with all other parameter settings also based in weeks. However, it is the ratio of replenishment
cycle time to process lead-time which is important. Hence if the lead-time was reduced by a factor of 7:1,
the simulation results are equivalent to daily order call-off as is typical in world-class retailing supply
chains (Sabath, 1995).
The seamless supply chain is crucial
The concept of a world-class company has changed immeasurably over the last century. The attributes
for attaining and maintaining such a position are no longer static, but are dynamic in many dimensions.
The marketplace is no longer an arena of a few players with loyal customers and well-established
trademarks. Customers are now far more demanding, requiring greater choice, quality, value for money
and timely delivery. The consumers now have a world marketplace to choose from and are notorious for
voting with their feet.
Each player in the supply chain is dependent on the patronage of the end consumer, so no matter how far
upstream a player resides, satisfying the customer at the marketplace should be key to their strategy.
Therefore the performance of the whole supply chain is crucial; a bad link can critically affect all members
of the chain. Towill (1997b) sums this up "To survive, let alone win, a company must be part of one or
more supply chains producing world class performance". Hence companies need to work together and
optimise the complete pipeline by establishing a seamless supply chain ("think and act as one") to
maximise their market share. Only with this support of the holistic chain concept can further significant
and radical improvements in individual business performance be realised.
The greatest opportunities for time compression are therefore at the holistic level. This includes lead-time
compression via information sharing. A time-based company is only as good as their fellow players in the
supply chain. The futility of becoming a time-based competitive player in an unchanging supply chain is
clearly shown in Figure 1. Here the simulation results for the four-level supply chain predict the effect of
implementing production cycle-time compression down to four weeks in the factory alone. It is manifest
that, although the factory has established a 60 per cent improvement in response time to its demand, the
impact on the rest of the supply chain is negligible.
In fact in this exemplar case the consumer at the marketplace would be hard put to detect that any
improvement had taken place anywhere within the supply chain. So in order to impact on the buying
habits of the end consumer, thereby increasing market share to the benefit in the chain of all players,
competing on time needs to be a holistic strategy. Furthermore engineering the 60 per cent reduction in
factory lead-time may also be very costly. It is frequently the case that the factory end of the product
delivery process (PDP) is already relatively efficient. For example, results from a UK electronic products
manufacturer show only 15 per cent non-value added time in the production process compared with much
larger percentages (up to 50 per cent) in other business processes (Hope and Hope, 1995).
The two pronged time compression attack maximises competitive advantage
All supply chains have two distinct lead-time pipelines, first, the order information transfer pipeline, from
point of sale to raw material supplier, and second, the product transfer from raw materials to end
customer. Production is activated by demand information, therefore speed and fidelity of order data
transference is crucial to an effective supply chain time compression strategy. As we have already seen
the two pipelines together result in the total effective lead-time of the supply chain determining supply
chain dynamics.
Figure 2 illustrates the characteristic U shape of the total lead-time pipeline, from end consumer demand
to goods delivery into the market place with orders flowing upstream and products flowing downstream.
Material flow is activated by order information, therefore speed and fidelity of information transference is
crucial to an effective time compression strategy.
In a seminal paper Braithwaite (1993) warns of the futility of striving to reduce manufacturing cycle times
by one day if the TCT compression programme does not tackle two- to three-week ordering delays. The
result leaves customer service levels substantially untouched after what may be frenzied shopfloor
activity. This is clearly demonstrated in Figure 3 which shows the long order information pipeline exhibited
by many companies. Note the potentially long lead-time even for items supplied from local stock since the
response time can be as high as 50 per cent of the make-to-order time.
Van Ackere et al. (1993) argued that managers can and should re-design their business processes to
gain competitive advantage and must include improved information flow within their new strategy. The
evidence from Figures 2 and 3 supports this view that the total time-based strategy should therefore
include optimisation of both the material flow lead-time and the order lead-time pipelines. Van Ackere et
al. also share our view of the importance of systems dynamics simulation as an essential tool for BPR of
supply chains. The various techniques exploited in the material flow lead-time will now be briefly
reviewed. Our approach follows that adopted by Towill (1996).
Material flow lead-time compression
Companies have appreciated the benefits of minimising their cycle times for many years. The example of
Henry Ford, presented earlier, shows that the automobile industry saw the light as far back as the 1920s.
There are four basic tactical approaches to cycle time compression available, as listed in Table II (Towill,
1996). The continuous flow line implemented by Ford was a combination of compression and integration.
However, as we have argued previously, the new output of TCT reduction is fundamentally different. The
effect of "economies of scope" is to enable customer choice to be met at reasonable cost.
The practice of material flow cycle time reduction is well established. The same principles have been
extended into areas that could not have been readily foreseen, such as commerce and insurance (Stalk
and Hout, 1990). Companies have found that reducing and continually improving cycle times positively
impacts on many other areas within the organisation such as inventory policies and workforce attitudes
(Womack et al., 1990). For example it is of little benefit speeding up the shopfloor if the internal
warehouse is slow and cumbersome at despatching orders. This in turn leads to greater work-in-process
(WIP), a ballooning warehouse, loss of response flexibility, but still dissatisfied customers. Frequently we
find the warehouse is full, but with the wrong items. We then risk losing money from obsolescence as well
as stock-out.
As is evident from Towill (1996), cycle time reduction has positively affected many operational facets
within individual company structures the challenge is now to take the lessons outside and apply them to
the whole supply chain. Applying TCT compression right across supply chain is obviously more complex
but the basic issues remain the same and the four strategic approaches of Table II still hold strong.
Practical TCT compression can be achieved through a variety of techniques which following Scott and
Westbrook (1991) are conveniently grouped as shown in Table III.The first three categories listed in Table
III are associated with material flow time compression activities, whereas information technology
improvements additionally tackle the order pipeline. The importance of the availability of undistorted
marketplace information is further emphasised with the addition of electronic point of sales (EPoSs) to the
original table in Towill (1996).
Order information time compression
Stalk and Hout (1990) specifically warn of the dangers of slow information lead-times, summing up the
problems with information delays when they state "The underlying problem here is that once information
ages, it loses value ... old data cause amplifications, delay and overhead ... The only way out of this
disjointed supply system between companies is to compress information time so that the information
circulating through the system is fresh and meaningful."
Market sales data are the catalyst information for the whole supply chain, holding undistorted data
describing the consumer demand pattern. The order information pipeline offers ample opportunity for time
compression and invariably has an unnecessarily long lead-time due to the way in which orders cascade
along the chain (Mason-Jones and Towill, 1997). Therefore the best way to compress the information
pipeline is to directly feed each player in the supply chain with the market sales data thereby eliminating
the traditional long pipeline. So rather than each player making his/her order decision based purely on the
internal chain order data he/she can now make an informed judgement based on what the end consumer
is actually buying at the point in time of sale. Not only is the speed of the order information pipeline vastly
improved, but also supply chain benefits such as stock reduction and greater flexibility are possible due to
the virtual elimination of the Burbidge Law of Industrial Dynamics, which states:
If demand for products is transmitted along a series of inventories using stock control ordering, then the
demand variation will increase with each transfer (Towill, 1997b).
Figure 4 shows a rich picture representation of the optimised order information pipeline, in which each
echelon receives the data directly. This technique we call the information enrichment model and is
achieved via an EPOS. Hence utilising the market sales information throughout the supply chain allows
all "players" to have an undistorted view of the consumer thereby enabling the supply chain to operate
effectively without the added burden of the magnification effect described by the "Law of Industrial
Dynamics".
The major technology behind improved information flow is the advent of electronic data interchange
(EDI). It offers greatly improved information flows and is an extremely important adjunct within leading
organisations in the drive to decrease lead-times (Evans et al., 1993). However, while the introduction of
EDI in many companies has offered marked improvement in the speed of transmission of orders as
documented by Macbeth and Ferguson (1994) the current information flow in the vast majority of supply
chains is still far from ideal.
Unfortunately many information strategies have involved far too much bias towards the technology used
as opposed to concentrating on fidelity and availability of the actual data transferred. The statement "Too
many managers believe that once the right technology is in place, appropriate information sharing will
follow" (Davenport, 1994) reflects this trend. EDI offers greater opportunities than just speeding up current
information transfer; it can be utilised to radically reassess the demand data flow through the whole
supply chain. A truly effective information flow time compression strategy must address the order
magnification problems experienced within supply chains if competitive advantage is to be optimised. This
cannot be enabled without significant attitudinal change amongst the players.
There is still much untapped mileage available to companies competing on time by using undistorted
point of sales information provided they are willing to embrace a new strategy for the data pipeline.
Successful time competitive supply chains must view their information as a strategic asset and ensure
that it flows with minimum delay and minimum distortion. The payoff is better customer service, reduced
stocks throughout the chain, and reduced risk of product obsolescence leading to enhanced business
performance by all "players".
Results of simulating total time compression
Analysing the response to a step demand is an essential starting point when simulating a supply chain
because of the relative ease of analysis and background of previous research. Therefore the simulation
model was injected with a 20 per cent step increase in consumer demand for each of the time
compression strategies. A step increase in market demand may be wrongly considered somewhat
artificial as a real world example readily demonstrates (Holmstrom et al., 1997). Furthermore using the
step response as a benchmark it is possible to thereby predict system responses for a wide range of
operating scenarios (Mason-Jones et al., 1998).
The supply chain simulation results offer conclusive evidence that to maximise time compression benefits
a TCT approach should be used. The material flow pipeline lead-time compression was simulated by
reducing each lag to the retailer settings, therefore all players now have a delivery lead-time of four
weeks. Order pipeline time compression was achieved using the information enrichment model illustrated
previously in Figure 3. The total time compression model implements the direct market data linkage at
each level plus reduced material flow lead-times. In this simulation model the orders placed by the next
echelon are satisfied but we additionally use marketplace data within our own decision support system.
Figure 5 clearly illustrates the benefits of time compression of each pipeline separately and then the
combined effect of implementing changes to both channels simultaneously. As expected reducing leadtimes in the material flow path improves the supply chain's response to the step up in demand although
this inherently causes the system to be more oscillatory. This is scarcely unanticipated because although
all players have improved their material flow lead-times they are still receiving stale, distorted data.
Conversely improving the information pipeline offers greater ordering control thereby reducing the "shock"
to the system induced by the demand. This is hardly surprising because an EPoS link to the factory order
decision point will ensure visibility of changing consumer habits as and when they occur. The "information
enriched" factories are therefore able to make an informed decision at a much earlier point in time than
the manufacturer at the end of the traditional supply chain.
Benchmarking performance improvement
As a step towards the provision of generic systems design guidelines we have benchmarked supply chain
performance for the various time compression scenarios. From Figures 2 and 4 it is very clear that the
associated problems with lead-time delays and magnification of the echelon outputs are most keenly
experienced by the furthest upstream player, in this case the factory. Because the simulated time
compression strategies are holistic in nature (i.e. benefit everyone) it is reasonable to judge the improved
response by analysing and comparing the resulting factory responses for various scenarios knowing that
there will be a consequential benefit throughout the chain.
In order to benchmark the strategies a methodology for ranking the designs was needed. Hence for each
benchmark the best and worst design were highlighted on a linear scale and were ranked accordingly
with four stars (best) and one star (worst). The remaining designs were then ranked on this scale by
reference to the best and worst designs as observed during the simulation. If the difference between the
two designs is regarded as insignificant, both designs are given the same star rating.
The combined effect of TCT in Figure 6 shows the dramatic benefits available. Not only have the
response times been greatly improved but the overshoot of the supply chain has reduced for both the
ordering function and the stock levels. The effect the production and information pipelines have on supply
chain behaviour is seen as significantly different. Information is crucial to the control of the supply chain
whereas material flow directly affects the speed of response. A TCT strategy combines the best features
of compressing each pipeline to offer a far healthier and competitive advantage. So the "product
champion" (Towill, 1992) has substantial evidence to support his/her drive for enabling supply chain time
compression via information sharing.
Squaring the dynamic response circle
In the real world a supply chain rarely has to contend only with the occasional step in demand. More often
consumer demand fluctuates randomly or in an upward/downward trend, or has seasonality components.
So to gain additional insight into the dynamic effects of time compression on the supply chain the four
time compression strategies were re-simulated with a random consumer demand at the marketplace. The
results show that by compressing material flow and information flow lead-times simultaneously we can
"square the dynamic response circle". That is we can significantly improve stock control and capacity
dynamics.
The factory order rate and stock level responses to the random demand signal are shown in Figure 7. As
expected the beneficial effects of time compression exhibited by the step response results are replicated
by the supply chain when subjected to a random demand. Note however that the random marketplace
orders trigger off pseudo-seasonal patterns of behaviour at the factory level. This is as expected from a
knowledge of system dynamics (Forrester, 1960). However, the "intelligent" product champion would
recognise that these patterns are self-induced by the supply chain, and would not be wrong-footed as to
the true causality of this phenomenon.
The trade-off matrix of Figure 8 very effectively summarises the benefits available for both orders and
stock level patterns, via time compression of each of the supply chain pipelines. Compression of the
material flow lead-time allows the supply chain to react much faster to changes in consumer demand.
Improvements in the order pipeline lead to far greater control of the supply chain response due to greater
visibility of the consumers behaviour. But taking the TCT approach to time compression additionally
results in improved reaction to consumer demand. Specifically we improve stock control dynamics
simultaneously with reducing capacity fluctuations, i.e. square the traditional dynamic response circle by
avoiding the need for a trade-off between stock and capacity performance measures.
Conclusions
Simulation results show that the main gains from time compression are only realised when a holistic
approach is taken, tackling both the production and the information pipeline. Implementing a time-based
strategy while residing in a slow supply chain will usually do relatively little to improve market share of an
individual company. However it may be the route to moving towards a forward thinking chain and thereby
ensure a bright future for all the players. To move successfully into the next century a supply chain has to
appeal to the end consumer and give him what he wants - the right product at the right price at the right
time.
Although much can be achieved from material flow cycle time reduction, reacting faster to a slow order
does not significantly improve control of the supply chain dynamic response to a change in consumer
demand. To improve control of the highly undesirable overshoot effects the supply chain has to provide
undistorted order information fast. Therefore to maximise and complement production cycle time
compression a supply chain must re-design its order information usage strategy.
Time compression of the order pipeline via direct utilisation of the market sales information by each player
in the supply chain certainly improves the overall speed of response and lessons the impact of the
demand magnification phenomena. Players no longer have to suffer from the distortions that infect
traditional supply chains. However, the real power of information flow only becomes evident when it is
utilised throughout the supply chain. It takes a visionary world-class supply chain to implement a total
time compression strategy, especially from the point of market information sharing for the holistic good.
However, for those who take the revolutionary step, the tremendous benefits to speed and control of
response to consumer demand will offer a strong competitive weapon.
Table I The benefits of being a time-based competitor
Figure 1 The impact on the supply chain from reducing the production lead-time in the factory
Figure 2 Traditional supply chain "U"-shaped total lead-time
Figure 3 Customer order turnaround times and cumulative cycle times
Table II Strategies for cycle time reduction
Table III Activities for time compression
Figure 4 Information pipeline time compression model
Figure 5 The effect of varying time compression strategies on supply chain orders
Figure 6 Performance improvement benchmarking for successive time compression strategies
Figure 7 Factory response to a random demand
Figure 8 Measure of performance matrix for random demand
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